This review explores causal decision-making to improve decision quality.
problem Effective decision-making requires understanding causal relationships.
method Causal structure learning, causal effect learning, and causal policy learning.
result Challenges in causal decision-making are identified and recent advances are discussed.
Improving cancer treatment decisions requires considering causal effects, not just model accuracy.
problem Cancer outcome prediction models may cause harm when used for treatment decisions.
method Explains the importance of considering causal effects in model validation and provides guidelines.
result Building and validating models that are useful for decision making requires considering causal effects.
Extends expected value framework for cost-sensitive causal decision-making.
problem Optimizing operational decision-making with cost-sensitive causal classification.
method Introduces a cost-sensitive decision boundary based on estimated individual treatment effects, positive outcome probability, and cost parameters.
result Effective in maximizing expected causal profit, outperforming cost-insensitive ranking approach.
Chronological Causal Bandits (CCB) tackles dynamic causal decision-making.
problem Dynamic causal decision-making in a system where rewards depend on past interventions.
method Introduces a new MAB problem (Chronological Causal Bandit) where rewards are influenced by a dynamic causal model.
result Early findings show the CCB can transfer information between sequential MABs.
New method estimates stochastic intervention effects in decision-making domains.
problem Current causal inference methods are limited to deterministic treatment, unable to handle stochastic policies.
method Developed a new stochastic propensity score and stochastic intervention effect estimator (SIE) with a customized genetic algorithm (Ge-SIO).
result Empirical study shows significant performance improvement over state-of-the-art baselines.
The paper introduces a new algorithm for fair decision-making in outcome control tasks.
problem Fair and equitable automated decision-making in outcome control tasks.
method Causal analysis and optimization to ensure fairness in decision-making.
result Developed an algorithm for maximizing Y Y Y while ensuring causal fairness. Introduces principal fairness for fair decision-making.
problem Discrimination among similarly affected individuals.
method Uses principal stratification from causal inference.
result Explicitly accounts for decision impacts, not just protected attributes.
New formalism for decision making combines causal structures with MDPs, improving reinforcement learning performance.
problem Sequential decision making with causal knowledge to improve performance.
method Causal Markov Decision Processes (C-MDPs) and C-UCBVI algorithm exploiting causal structure.
result C-UCBVI achieves an i l d e O ( H S Z T ) ilde{O}(HS\sqrt{ZT}) i l d e O ( H S Z T ) regret bound, independent of actions. Framework identifies causal factors of climate change using correlations and machine learning.
problem Understanding socioeconomic factors influencing carbon emissions and climate change.
method Three-step framework: correlation analysis, causal discovery, LLM interpretations.
result Adaptable solutions for data-driven policy-making and strategic decision-making.
New methods improve robust decision-making under uncertainty in off-policy evaluation.
problem Statistical uncertainty and causal considerations in off-policy evaluation.
method Marginal Ratio (MR) estimator, Conformal Off-Policy Prediction (COPP), causal bounds.
result Improved robustness and uncertainty quantification in off-policy decision-making.
Survey on causal interpretability models for machine learning.
problem Understanding and explaining the decisions made by machine learning models.
method Comprehensive survey on causal interpretable models.
result Evaluation metrics for interpretability are discussed.
Causal Bayesian Optimization improves global optimization with causal information.
problem Optimizing a system with causal relationships among variables.
method Combines causal inference, uncertainty quantification, and sequential decision making.
result Causal information significantly improves optimization strategies and reduces costs.
A fundamental question for companies with large amount of logged data is: How to use such logged data together with incoming streaming data to make good decisions? Many companies currently make decisions via online A/B tests, but wrong decisions during testing hurt users' experiences and cause irreversible damage. A ty…
Bayesian method optimizes interventions for causal discovery.
problem Active interventions are needed for causal discovery when observational data is insufficient.
method Bayesian optimization-based approach using observational data and pre-experimental evaluation of interventions.
result Demonstrated effectiveness through various experiments.
Introduces causal fairness analysis to address unfairness in AI decisions.
problem Fairness issues in AI decision-making.
method Link observed disparities to underlying causal mechanisms.
result Develops Fairness Map and Fairness Cookbook.
This study integrates cost-sensitive and causal classification methods.
problem Improving classification model performance in business decision-making.
method A unifying evaluation framework for cost-sensitive and causal classification.
result Conventional classification is a specific case of causal classification.
A textbook on machine learning explaining patterns, predictions, and actions.
problem Understanding patterns in data for predictions and actions.
method Explains foundations of decision making, representation, optimization, and generalization.
result Equips readers with tools to reason about actions and their consequences.
Causal thinking improves healthcare decisions from EHRs.
problem Shortcuts in data lead to biased healthcare decisions.
method Step-by-step framework for valid decision making from EHRs.
result Valid decision making requires careful analysis of EHR data.
The paper tackles individualized decision-making under unmeasured confounding, providing a novel minimax solution and a paradox.
problem Unmeasured confounding in causal inference leads to biased estimates and affects individualized decision-making.
method The authors establish a formal link between individualized decision-making under partial identification and classical decision theory, providing a minimax solution and a paradox.
result A novel minimax solution for individualized decision-making/policy assignment is provided, and an interesting paradox is drawn.
Optimal treatment regime uses proxy variables to improve decision-making.
problem Insufficient covariates in observational data lead to confounding issues.
method Proximal causal inference framework and outcome/treatment confounding bridges.
result The proposed optimal treatment regime outperforms existing ones.
CPP improves predictive model outputs for better intervention decisions.
problem Predictive models often misguide resource allocation.
method Causal post-processing techniques using limited experimental data.
result CPP can improve intervention decisions, especially with imperfect causal signals.
Causal ML predicts treatment outcomes, aiding personalized medicine.
problem Predicting individualized treatment effects for personalized medicine.
method Flexible, data-driven methods using causal inference with clinical trial and real-world data.
result Causal ML allows for estimating individualized treatment effects.
Paper proposes Bayesian TMLE methods for causal effect uncertainty quantification.
problem Quantifying uncertainty in causal effect estimation.
method Three Bayesian TMLE approaches for binary and continuous outcomes.
result BN-TMLE outperforms classical implementations in small data regimes.
Applying machine learning in the health care domain has shown promising results in recent years. Interpretable outputs from learning algorithms are desirable for decision making by health care personnel. In this work, we explore the possibility of utilizing causal relationships to refine diagnostic prediction. We focus…
New method handles uncertainty in causal effect estimation for better decision-making.
problem Handling uncertainty in causal effect estimation, especially in high-dimensional data and covariate shift.
method Integrates uncertainty estimation into neural network methods for individual-level causal estimates.
result Uncertainty-aware methods improve decision-making by alerting when predictions are not reliable.
DCBO optimizes interventions in evolving causal systems.
problem Optimal interventions in time-varying causal systems.
method Combines sequential decision making, causal inference, and GP emulation.
result DCBO identifies optimal interventions faster than competitors.
New method uses causal thinking to make AI fairer decisions.
problem Designing fair machine learning models that treat equal individuals equally and unequals unequally.
method Rank-preserving interventional distributions and warping method.
result Warping method effectively identifies discriminated individuals and mitigates unfairness.
DECI combines causal discovery and inference in a single model for diverse data types.
problem Combining causal discovery and inference methods for diverse data types.
method Develops a single flow-based non-linear additive noise model (DECI) for causal discovery and inference.
result DECI can recover ground truth causal graphs and perform (C)ATE estimation.
Bayesian Supervised Causal Clustering identifies patient subgroups for personalized decision-making.
problem Finding patient subgroups with similar characteristics for personalized decision-making.
method Bayesian Supervised Causal Clustering (BSCC) that identifies homogenous subgroups based on treatment effects.
result BSCC identifies subgroups with similar covariate profiles and treatment effects.
A new algorithm tackles delayed combinatorial semi-bandit with causal relations.
problem Optimizing decisions in a non-stationary environment with delayed and causally related rewards.
method Formalized as a non-stationary delayed combinatorial semi-bandit problem, the approach models causal relations with a directed graph in a stationary structural equation model. The agent learns these relations from delayed feedback to optimize decisions.
result Proved a regret bound for the proposed algorithm's performance.
The paper proposes Tier Balancing for dynamic fairness in decision-making.
problem Achieving long-term fairness in decision-making processes.
method Causal modeling with DAGs to investigate dynamic fairness.
result Tier Balancing is a more natural approach to achieve long-term fairness, capturing latent causal factors.
Discovering the causal effect of a decision is critical to nearly all forms of decision-making. In particular, it is a key quantity in drug development, in crafting government policy, and when implementing a real-world machine learning system. Given only observational data, confounders often obscure the true causal eff…
A framework for causal classification using uplift and causal heterogeneity methods.
problem Predicting the effect of interventions on different individuals from data.
method Causal classification framework using off-the-shelf supervised methods.
result Framework works for causal classification and uplift modelling, competitive with other methods.
New approach to off-policy evaluation connects causal graph to policy effects.
problem Evaluating policies using observational data from different policies.
method Formalizes off-policy evaluation within a causal graph framework.
result Identifies specific causal estimands and highlights necessary experimental data.
In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' propensity to "game" the decision rule by changing their features so as to receive better decisions. Whereas the strategic classification literature has previously…
The paper tackles finding optimal treatment sequences in continuous state spaces.
problem Finding counterfactually optimal action sequences in continuous state spaces.
method Formalizes the problem using finite horizon Markov decision processes and structural causal models. Develops a search method based on the A* algorithm.
result The method can find optimal action sequences in polynomial time under certain conditions.
Proposes a model to estimate effects of multiple related treatments.
problem Estimating effects of many related treatments in observational data.
method Customized ridge regression to reduce noise and MSE.
result Significantly reduces MSE for individual sub-treatments while allowing reconstruction of aggregated treatment effects.
This paper identifies the minimal set of nodes for optimal conditional interventions in causal bandits.
problem Optimizing decision-making in causal bandits with conditional interventions.
method Graphical characterization and efficient algorithm to identify the minimal set of nodes.
result The proposed algorithm significantly prunes the search space and accelerates convergence rates.
Aims to improve personalized treatment decisions through Bayesian experimental design.
problem Evaluating and improving personalized treatment decisions in contexts like customer service.
method Model-agnostic Bayesian Experimental Design to efficiently gather data and avoid highly sub-optimal treatments.
result Our method achieves superior performance in evaluating and improving treatment decisions compared to traditional approaches.
Review of methods enabling causal predictions under hypothetical interventions.
problem Need for predicting outcomes under hypothetical interventions in decision making.
method Systematic review of methods using causal inference for prediction models.
result Identified 13 methods for causal inference from observational data.
As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether th…
Bridges uplift modeling and sequential decision-making with online budget allocation.
problem Treatment allocation under budget constraints in digital advertising.
method Budget-Constrained Causal Bandits (BCCB) integrates learning, exploration, and budget pacing.
result Data-efficiency crossover: BCCB operates effectively from the first user, 3-5x lower performance variance.
Crowdsourced wisdom improves causal learning.
problem Improving causal learning through collective intelligence.
method Crowdsourcing, expert knowledge elicitation, aggregation techniques, and LLMs.
result Collective contributions enhance global causal structure.
CDM and CEE differ; accurate CEE isn't needed for CDM.
problem Understanding the difference between CDM and CEE.
method Highlighting the distinctions and implications of CDM and CEE.
result Accurate CEE is not necessary for accurate CDM.
Machine learning based decision making systems are increasingly affecting humans. An individual can suffer an undesirable outcome under such decision making systems (e.g. denied credit) irrespective of whether the decision is fair or accurate. Individual recourse pertains to the problem of providing an actionable set o…
Develops methods for personalized treatment decisions in the presence of unmeasured factors.
problem Personalized treatment decisions in the presence of unmeasured confounding.
method Proximal learning approaches to estimate optimal individualized treatment regimes (ITRs).
result Established identification results for different classes of ITRs, improving decision-making value function.
BICauseTree improves causal effect estimation by identifying clusters and balancing treatment allocation.
problem Improving interpretability and transparency in causal effect models from observational data.
method Hierarchical bias-driven stratification using decision trees with a customized objective function.
result BICauseTree provides interpretable causal effect estimation and is comparable to existing methods.
New clustering method considers causal fairness to avoid bias.
problem Clustering algorithms can unintentionally propagate unfair disparities.
method Integrates causal fairness metrics into clustering algorithms.
result Demonstrates efficacy on datasets with known unfair biases.